Determining the alertness of a user is one of the areas for research. One of the examples for determining the alertness is a driver in a vehicle using. Fatalities have occurred as a result of car accidents related to driver inattention, such as fatigue and lack of sleep. Physiological feature-based approaches are intrusive because the measuring equipment must be attached to the driver. Physiological feature based approaches utilize visual technologies to determine the alertness of the user. Thus, physiological feature-based approaches have recently become preferred because of their non-intrusive nature.
The existing models of driver alertness monitoring (DAM) system relies on detection of frontal face using a camera that is placed in front of the driver. Most of the existing methods use CAMSHIFT algorithms that rely on the skin color. Therefore, tracking is efficient in day light. The other problem with optical flow tracking is the key points on the face always tends to be missed if there is jerk in driving. The optical flow tracking is not so reliable if someone rubs hand over the face. The key points in a face are dragged by the hand showing wrong alert.